Trauma exposure among individuals with mood disorders: a cross-sectional study at a tertiary psychiatric facility in Uganda
Bibliographic record
Abstract
BACKGROUND: Mood disorders, including major depressive disorder (MDD) and bipolar disorder (BD), are prevalent psychiatric conditions globally. Exposure to trauma has been shown to exacerbate the severity and chronicity of mood disorders. In low-resource settings like Uganda, where trauma exposure is widespread and mental health services are limited, understanding the trauma-mood disorder relationship is essential but under-researched. AIMS: This study aimed to assess the prevalence and typology of trauma exposure among individuals with mood disorders attending a tertiary psychiatry unit in Uganda and to explore correlates of trauma history. METHODS: A cross-sectional study was conducted among 221 adults diagnosed with mood disorders at Mbarara Regional Referral Hospital between April and June 2023. Participants were assessed for trauma exposure using the Stressful Life Events Screening Questionnaire (SLESQ), and suicidality was evaluated using the Columbia-Suicide Severity Rating Scale (C-SSRS). Socio-demographic and clinical variables were collected, and data were analyzed using descriptive statistics, chi-square tests, t-tests, and logistic regression. RESULTS: Among 221 participants, 72.0% reported lifetime trauma, most commonly crime-related events (95.6%). Physical abuse was significantly more common in bipolar disorder than in major depression. Older age and family history of suicide attempt independently predicted trauma exposure. CONCLUSION: Trauma exposure, especially crime-related, is highly prevalent among Ugandan patients with mood disorders. Findings highlight the need for routine trauma screening and trauma-informed care in psychiatric services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".